I have attached all necessary information in Statitics assignment in word. 
Due date : 2nd April 2022
Reference: APA style
Word count: 700-1000 words
I have attached others student work that was provided by lecturer. I have also attcahed an assigned variable. My student id ends with 7. So please refer to the topic from the variable assigned.
Please let me know if you need any information for this assignment also also a quote for the assignment.

Statistics Assignment

The purpose of this assignment is to give you an opportunity to demonstrate your skills in describing and analysing data using concepts and tools that we have developed in the course so far.
Below are instructions on how to collect a specified set of data and what to do with it.  Your goal is to produce a report in MS Word discussing the data and submit this along with a single MS Excel workbook showing your workings.   A suggested target range for the word count of the report is 700-1000 words.
I have prepared and attached an example Excel workbook which I will refer to below. Note: my Excel workbook is not a model answer.  You may choose to use different visualisations and do not necessarily need all the computed statisitcs and charts I have included.  It really depends on the features of the data you have, so you need to use your own judgement as to how to best present and describe the data.  Besides, the primary output for this assignment is the report itself, not the workbook.

Data collection

Collect quantitative data on two variables from the Sustainable Development Report 2021 website.
· Go to 
 and browse around the site to become familiar with its purpose and the information publicly available there.
· Go to the “Downloads” page and click on “Database EXCEL” to download the database of indicators used to assess countries’ progress towards the UN Sustainable Development Goals.  You will be taking data from the “SDR2021 Data” sheet in the workbook. Starting from column AR of that sheet, there are columns of cross-country data for the SDG indicators, one row for each country.  Note that from row 195 the data are for regional blocs so these should be excluded from the data you take.
· You have been assigned two variables according to the last digit of your Student ID number.  You can find the variables assigned to you in the attached file “Assigned variables.xlsx”.  For example, my student ID number (a long long time ago in a galaxy not so far away) ended with 2 so I would be using variables “Poverty headcount ratio at $1.90/day (%)” and “Cereal yield (tonnes per hectare of harvested land)”.  I have chosen pairs of variables that may potentially have a statistical relationship.  If you wish you are welcome to switch one of the variables with another one from the database that you are interested in investigating and you think is related to the variable you retain.

· P.S: My student number ends with 7. Therefore, my topics are


Corruption Perception Index (worst 0-100 best)


Population with access to clean fuels and technology for cooking (%)


· Please refer to Variable Assigned excel sheet for more information.

· Look up your variables in the Data Explorer on the website or in the report from page 75 (some newer variables are not included on the website yet, it seems).  The main thing you want to understand is what a given value of each of your variable


· Check that you included everything that was asked for in the report (not just the workbook). If you missed out computing or discussing e.g. the p-values I couldn’t give you marks for those!

Data description – univariate

· Things I looked for were
· A brief introduction to the variables: what do the quantities mean?
· Use of descriptive statistics to describe the main features of the data e.g. IQR, CV, mean, median, quartiles, standard deviation (together with empirical rule or Chebyshev theorem)
· A little discussion about outliers that were interesting or were removed
· Histograms, polygons, boxplots and/or normal probability plots and discussion of the data distribution shape
· If there was no observation in a data set for a country it should not be treated as zero. Also be suspicious of zeros that do appear in the raw data set – they might have ended up there in place of no observation.
· Be careful with units – state what the units of the variables are and keep using those units for things like mean, standard deviation etc. E.g. cereal yield is in tonnes per capita, not percentage.
· Left skewed or negatively skewed data has a peak near the top of the distribution and a long lower tail.
· A data set does not have to fall into {left skewed, symmetric/normal, right skewed}. There are many other variations (without specific names, you could just call it asymmetric for instance).
· It wasn’t necessary to transform a data set for the univariate analysis if it was skewed, only if it was so skewed or outliers were so extreme that boxplots etc. were not useful. It could of course be useful for bivariate analysis if one or both variables seem to be lognormal, because then you could still see a linear relation in the scatterplot using the logged variable(s) and linear correlation and regression would be valid.

Data description – bivariate

· Things I looked for were
· A scatterplot and associated discussion (not a line chart or bar chart with series side by side)
· The correlation coefficient being stated and interpreted
· Discussion about why there might or might not be a relationship
· You shouldn’t just rely on the correlation coefficient – if the scatterplot indicates almost no relationship then you should say it is doubtful there is an actual relationship
· If the scatterplot indicates there is a relationship, but it is more likely to be non-linear, you should mention this.
· Don’t just say there might be a third variable and leave it at that. Some discussion is important to show you know what this actually means.

Confidence intervals

· Things I looked for were
· Statement of each confidence interval in a sentence, in the context of the variable
· Discussion of why the confidence intervals were valid (appealing to CLT)
· Some said the sample mean was a good estimate because it was inside the confidence interval. Of course, the sample mean is at the center of the interval! This does not guarantee anything about the accura


Last Digit of Student ID Variables SDG # List of SDGs

0 Poverty headcount ratio at $1.90/day (%) 1 SDG 1 Poverty headcount ratio at $1.90/day (%)

Mean area that is protected in freshwater sites important to biodiversity (%) 15 Poverty headcount ratio at $3.20/day (%)

1 Maternal mortality rate (per 100,000 live births) 3 Poverty rate after taxes and transfers (%)

Mean area that is protected in terrestrial sites important to biodiversity (%) 15 SDG 2 Prevalence of undernourishment (%)

2 Poverty headcount ratio at $1.90/day (%) 1 Prevalence of stunting in children under 5 years of age (%)

Cereal yield (tonnes per hectare of harvested land) 2 Prevalence of wasting in children under 5 years of age (%)

3 Mortality rate, under-5 (per 1,000 live births) 3 Prevalence of obesity, BMI ≥ 30 (% of adult population)

Birth registrations with civil authority (% of children under age 5) 16 Human Trophic Level (best 2-3 worst)

4 Neonatal mortality rate (per 1,000 live births) 3 Cereal yield (tonnes per hectare of harvested land)

Government revenue excluding grants (% of GDP) (countries other than high-income and OECD DAC) 17 Sustainable Nitrogen Management Index (best 0-1.41 worst)

5 Prevalence of obesity, BMI ≥ 30 (% of adult population) 2 * = description not given on website, but is available in the report from p75 Yield gap closure (% of potential yield)

Homicides (per 100,000 population) 16 SDG 3 Maternal mortality rate (per 100,000 live births)

6 Fish caught from overexploited or collapsed stocks (% of total catch) 14 Neonatal mortality rate (per 1,000 live births)

Poverty headcount ratio at $1.90/day (%) 1 Mortality rate, under-5 (per 1,000 live births)

7 Corruption Perception Index (worst 0-100 best) 16 Incidence of tuberculosis (per 100,000 population)

Population with access to clean fuels and technology for cooking (%) 7 New HIV infections (per 1,000 uninfected population)

8 Logistics Performance Index: Quality of trade and transport-related infrastructure (worst 1–5 best) 9 Age-standardized death rate due to cardiovascular disease, cancer, diabetes, or chronic respiratory disease in adults aged 30–70 years (%)

Mean area that is protected in freshwater sites important to biodiversity (%) 15 * Age-standardized death rate attributable to household air pollution and ambient air pollution (per 100,000 population)

9 Government revenue excluding grants (% of GDP) (countries other than high-income and OECD DAC) 17 Traffic deaths (per 100,000 population)

Municipal solid waste (kg/capita/day) 12 Life expectancy at birth (years)

Example CO₂ emissions embodied in fossil fuel exports (kg/capita) 13 Adolescent fertility rate (births per 1,000 females aged 15 to 19)

Literacy rate (% of population aged 15 to 24) 4 Births attended by skilled health personnel (%)

Surviving infants who rec

Raw data

Country Code ISO3 Country CO₂ emissions embodied in fossil fuel exports (kg/capita) Literacy rate (% of population aged 15 to 24)

AFG Afghanistan 36.865 65.421

AGO Angola 121.699 77.431

ALB Albania 0.002 99.33

AND Andorra 0.709

ARE United Arab Emirates 6586.959 99.43

ARG Argentina 24.254 99.506

ARM Armenia 0 99.847

ATG Antigua and Barbuda 0

AUS Australia 42218.073

AUT Austria 283.9

AZE Azerbaijan 1901.54 99.938

BDI Burundi 0 88.222

BEL Belgium 0.001

BEN Benin 0 60.948

BFA Burkina Faso 0 58.29

BGD Bangladesh 94.862

BGR Bulgaria 19 97.865

BHR Bahrain 0 99.687

BHS Bahamas, The 0

BIH Bosnia and Herzegovina 79.427 99.662

BLR Belarus 0.001 99.85

BLZ Belize 0.002 84.223

BOL Bolivia 2684.402 99.399

BRA Brazil 2.298 99.204

BRB Barbados 0.001 99.9

BRN Brunei Darussalam 39474.108 99.709

BTN Bhutan 93.091

BWA Botswana 87.099 97.456

CAF Central African Republic 0 38.269

CAN Canada 3617.818

CHE Switzerland 0

CHL Chile 102.277 99.009

CHN China 16.731 99.784

CIV Cote d’Ivoire 0.001 58.42

CMR Cameroon 0.001 85.08

COD Congo, Dem. Rep. 0 84.99

COG Congo, Rep. 0.021 82.055

COL Colombia 4137.096 98.852

COM Comoros 0 78.27

CPV Cabo Verde 0 98.111

CRI Costa Rica 99.43

CUB Cuba 99.875

CYP Cyprus 0 99.821

CZE Czech Republic 617.289 99.786

DEU Germany 231.497

DJI Djibouti 0

DMA Dominica 0

DNK Denmark 0.009

DOM Dominican Republic 0 98.839

DZA Algeria 941.056 97.427

ECU Ecuador 0.012 99.255

EGY Egypt, Arab Rep. 54.232 88.193

ERI Eritrea 0 93.272

ESP Spain 44.738 99.716

EST Estonia 0.006 99.949

ETH Ethiopia 0 72.755

FIN Finland 0

FJI Fiji 0 99.744

FRA France 0.731

FSM Micronesia, Fed. Sts. 0

GAB Gabon 89.784

GBR United Kingdom 282.6

GEO Georgia 0.786 99.641

GHA Ghana 0.002 92.491

GIN Guinea 0 46.295

GMB Gambia, The 0 67.161

GNB Guinea-Bissau 0 60.402

GNQ Equatorial Guinea 97.967

GRC Greece 5.965 99.16

GRD Grenada 0 99.2

GTM Guatemala 0 94.354

GUY Guyana 0 96.69

HND Honduras 0 96.516

HRV Croatia 82.514 99.724

HTI Haiti 0 82.994

HUN Hungary 354.509 98.8

IDN Indonesia 3049.223 99.707

IND India 1.364 91.664

IRL Ireland 11.229

IRN Iran, Islamic Rep. 354.587 98.101

IRQ Iraq 0.049 93.5

ISL Iceland 0

ISR Israel 26.008

ITA Italy 7.913 99.93

JAM Jamaica 0 96.3

JOR Jordan 0.715 99.344

JPN Japan 0.064

KAZ Kazakhstan 4885.162 99.897

KEN Kenya 0 87.83

KGZ Kyrgyz Republic 157.853 99.75

KHM Cambodia 0 92.212

KIR Kiribati 0

KNA St. Kitts and Nevis 0

KOR Korea, Rep. 1.023 99.954

KWT Kuwait 0.28 99.08

LAO Lao PDR 48.92 92.463

LBN Lebanon 0 99.752

LBR Liberia 0 55.399

LBY Libya 1089.092 99.6

LCA St. Lucia 0

LIE Liechtenstein

LKA Sri Lanka 0 98.782

LSO Lesotho 0 86.632

LTU Lithuania 0.001 99.856

LUX Luxembourg 0

LVA Latvia 0 99.82

MAR Morocco 0 97.73

MCO Monaco

MDA Moldova 0 99.811

MDG Madagascar 0 81.198

MDV Maldives 0 98.754

MEX Mexico 583.843 99.318


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